In the current information rich climate, a considerable amount of attention is being paid to develop improved methods of information retrieval. In particular, the field of agent technology is heavily involved in developing agents for searching, summarisation, filtering and presentation of information e.g. Davies, Weeks & Revett, 1997 (“Information Agents for the WWW” in Software agents and soft computing, Eds H. Nwana, N. Azarmi, Berlin Springer-Verlag). Most, if not all, of these rely on the agent having some knowledge of the user. The inclusion of user information is rapidly becoming a key area, not only for agent technology, but also for the Internet in general, as demonstrated by the recently proposed Open Profiling Standard Dunn, Gwertzman, Layman & Partovi 1997 (“Privacy and profiling on the web” Technical note, WWW consortium Jun. 2, 1997). User information is undoubtedly playing an ever-increasing role as digital libraries, electronic commerce, and personalised applications become more widespread.
Filtering agents (such as email prioritising agents) were typically the first generation of agents that were concerned with managing volumes of information. Maes 1994 describes the various motivations, methods and applications of such agents for reducing information overload (Maes (1994) “Agents that reduce workload and information overload” Communications of the ACM, July). Indeed, this is perhaps the single most important reason that agents have succeeded within the Internet environment: there are simply too many resources available for any one person to consult exhaustively, and indeed if one were fortunate enough to have browsed all these pages, one would probably find that only a small percentage were actually of any interest or relevance. Search agents (such as Amalthaea) are becoming increasingly prominent as a means to reduce this workload, with most including some method of allowing the user to inform the agent of their interests and preferences. Further developments within agent technology have begun to consider previous interactions with a user as well as learning their interests.
Many recent agents include some form of user profiling, such as                Yenta (Foner & Crabtree, 1997 (“Multi-agent matchmaking” in Software agents and soft computing, Eds H. Nwana, N. Azarmi, Berlin Springer-Verlag)),        Syskill & Webert (Pazzani and Billsus, 1997 “Learning and revising user profiles: the identification of interesting web sites”, Machine learning 27 (3)),        Personal WebWatcher (Mladenic, 1996 “Personal webwatcher: design and implementation”, Technical report ljs-DP-7472, School of computer science, Carnegie-Mellon University, Pittsburgh USA, October.),        Letizia (Lieberman. 1995 “Letizia: An agent that assists web browsing”, Proceedings, 14th Joint International Conference on artificial intelligence (IJDAI-95 ), Montreal, Canada),        NewsSIEVE (Haneke, 1997 “Learning based filtering of text information using simple interest profiles”, In P. Kandzia & M. Klusch, Co-operative information agents. Berlin: Springer-Verlag”), and        INSOP (Kindo etal 1997 “Adaptive personal information filtering system that organizes personal profiles automatically”, Proceedings 15th International Joint Conference on Artificial Intelligence (IJCAI-97) pp. 716-721 Nagoya, Japan).The agent of Syskill & Webert learns a user's profile and uses this to guide its suggestions for interesting web pages. The user rates the relevance of the suggestions to the user's interest and the agent employs a naive Bayesian classifier to revise the user profile accordingly. Amalthaea (Moukas, 1997 (Moukas (1997) “User modelling in a multiagent evolving system” Proceedings, workshop on Machine learning for user modelling, 6th International Conference on User modelling, Chia Laguna, Sardinia)) employs a weighted keyword representation for a user's profile, which is then consulted in order to query Internet search engines in order to retrieve pages of interest to a user. Amalthaea's user profile comprises a number of information filtering agents (IFAs), each one specialised for a particular topic. These IFAs can be constructed in a number of ways (e.g. through analysis of a user's hotlist of favourite web pages; through observation of the user's interaction with their browser (using page access history logs) etc.) Furthermore the IFAs can evolve over time, so that they can adapt more closely to the user as the user interacts with Amalthaea over time.        
Many web sites collect information about a user in order to a) keep track of demographic information, b) provide personalised services and information from the site. This technique is becoming more important for sites to be able to maintain a loyal customer base. Indeed Firefly, Autonomy and OpenSesame! have all recently announced products which will enable such adaption.
It is therefore clear that having a profile of a user is crucial in order to provide services and information of interest to the user. Although the systems described above use a variety of techniques and information sources, not many systems attempt to arrange the keywords, or identifiers in the profile, as a function of the context of a user. The context of a user at a moment in time largely determines the type and content of information that is of interest to the user at that moment in time (e.g. if a user is working then the user is likely to want to know about work related interests) and developments in this area could assist in reducing the volume of information that is presented to a user.
The Applicant's patent EP807291 (IPD case ref A24976) teaches a software-based system known as the “JASPER” agent, which stores meta-information, such as URL of the document and keywords indicative of the content of the document, relating to documents of interest to a user. JASPER also stores user profiles comprising keywords that indicate the interests of users in particular types of information, and performs collaborative filtering between users to identify users that may have overlapping interests (arranging users into groups). JASPER compares the meta-information between groups of users, and, if one user in group A has identified a document as being relevant to him, JASPER passes the meta-information relating to the identified document to other users in group A (e.g. via email). The user profile can be arranged to store keywords in categories, as a function of context (e.g. types of work, leisure), and JASPER can change the content of these categories in accordance with detected changes in information type viewed by the user (e.g. performing a comparison between keywords stored in respect of one context and comparing that to the keywords in documents being viewed). JASPER is thus concerned with identifying a set of keywords that characterise a context, for the purposes of information delivery and identification between groups of users.
In the following description, the terms “interest”, “user profile”, “information source”, “context”, “instantiated interest” are used and are defined as follows:    “interest” includes subject-related data such as a set of keywords and/or images and/or music that are representative of a subject. Commonly images and music are accompanied by some form of description e.g. for music “Faure Requiem, written in 1887, the piece reflects Faure's vision of death”. In this case, keywords could be extracted from the description, and used to describe an interest (e.g. for the subject Impressionist, Death).    “user profile” includes one or more interests stored in respect of a user.    “information source” includes an entity that contains information, e.g. a document.    “context” includes representations that describe the current status of a user—e.g. state (work, play), situation (home, away) and company (colleagues).    “instantiated interests” when the status of the user has been identified, the context of the user is instantiated—e.g. the user is identified to be working at home, so the context is instantiated to work, home. Interests are then filtered according to the instantiated context and these interests are referred to as instantiated interests.
According to the present invention there is provided a method of changing a profile representing subject matter of interest to a user is provided. The profile includes a plurality of sets of subject-related data, and the method includes    (i) suggesting a change to the profile to the user, which suggestion includes one or more selection choices representative of the suggested change,    (ii) receiving one or more selection choices from the user,    (iii) modifying the user profile in accordance with the or each selection choice,    (iv) monitoring user action in respect of the modification, and, if the user action satisfies predetermined criteria, and    (v) permanently changing the profile in accordance with the or each selection choice.
Preferably the monitoring element (iv), which occurs with a frequency that is configurable by the user, includes    presenting the user with at least one further selection choice,    receiving the or each further selection choice,    comparing the or each further selection choice with the predetermined criteria.In addition, the or each further selection choice includes affirmation of the one or more selection choices representative of the suggested change suggested in (i).
Alternatively the monitoring element (iv) can comprise counting a number of times the user accesses information relating to the selection choice, and comparing the said number with a threshold number. As a further alternative, the monitoring element (iv) can include observing patterns of user access relating to the selection choice, and comparing the observed pattern with one or more predetermined patterns. Such patterns may include, for example, time spent accessing information relating to the selection choice, time of day that information relating to the selection choice is accessed, and repeatability of the way in which the user accesses information relating to the selection choice.
Preferably the method further comprises performing at least one of comparative analysis and/or cluster analysis between profiles of two or more users, and receiving output therefrom. This output is then used to provide at least one of the selection choices representative of the suggested change. Thus, for example, if interest A is linked with interest B and a user U1 has interest A, then interest B would be a suggested change. Other data mining and analysis methods could be used to identify potential interests, such as fuzzy logic, heuristic and knowledge based methods.
Advantageously the method further comprises evaluating confidence values associated with the output from the comparative analysis and/or cluster analysis, and the element (i) of suggesting a change to the contents of the profile based on the said output is performed in dependence on the evaluated confidence values associated therewith. Such confidence values may, for example, be calculated using statistical techniques, in a manner known to one skilled in the art.
Conveniently the method further comprises comparing the output with a list of changes that are deemed not to be relevant to the user, and, if any of the selection choices in the output matches any on the list, discarding the matching selection choices from the output. Thus interest B would be compared with a list of “non-interests”, and only suggested to the user if it has not been entered on the “non-interests” list.